Neural networks (a form of connectionist AI) can learn in different ways depending on the data and feedback available. There are four main training approaches: supervised learning, unsupervised learning, reinforcement learning, and self-supervised learning. These approaches differ in how the learning signal is provided, whether through correct output labels, inherent data patterns, reward feedback, or the model’s own generated signals.